熊猫在列上应用时间分组器

Question

Lets consider the following dataframe:

data={'close': 1.16155,
  'datetime': Timestamp('2017-11-01 22:29:40'),
  'high': 1.16155,
  'low': 1.16155,
  'open': 1.16155,
  'symbol': 'European Monetary Union Euro - United States dollar',
  'volume': -1.0},
 {'close': 1.00325,
  'datetime': Timestamp('2017-11-01 22:29:40'),
  'high': 1.00325,
  'low': 1.00325,
  'open': 1.00325,
  'symbol': 'United States dollar - Swiss franc',
  'volume': -1.0},
 {'close': 1.324475,
  'datetime': Timestamp('2017-11-01 22:29:40'),
  'high': 1.324475,
  'low': 1.324475,
  'open': 1.324475,
  'symbol': 'British pound - United States dollar',
  'volume': -1.0},
 {'close': 1.324475,
  'datetime': Timestamp('2017-11-01 22:29:45'),
  'high': 1.324475,
  'low': 1.324475,
  'open': 1.324475,
  'symbol': 'British pound - United States dollar',
  'volume': -1.0},
 {'close': 1.16155,
  'datetime': Timestamp('2017-11-01 22:29:45'),
  'high': 1.16155,
  'low': 1.16155,
  'open': 1.16155,
  'symbol': 'European Monetary Union Euro - United States dollar',
  'volume': -1.0}]
df=pd.DataFrame(data)

I would like to use groupby to group by symbol and datetime , without setting the index as either of symbol or datetime .

Ideally the result should be something like that: df.groupby(["symbol",pd.TimeGrouper("30T","datetime")]).count() .

• I know it can be done by doing
  df.set_index("datetime).groupby(["symbol",pd.TimeGrouper("30T")]).count()

But again, I would like to do it without setting the index to datetime or symbol .

Thx!

Answer 1

Is that what you want?

In [198]: df.groupby(["symbol",pd.TimeGrouper("30T", key="datetime")]).count()
Out[198]:
                                                                        close  high  low  open  volume
symbol                                             datetime
British pound - United States dollar               2017-11-01 22:00:00      2     2    2     2       2
European Monetary Union Euro - United States do... 2017-11-01 22:00:00      2     2    2     2       2
United States dollar - Swiss franc                 2017-11-01 22:00:00      1     1    1     1       1

or using Grouper :

In [203]: df.groupby(["symbol",pd.Grouper(freq="30T", key="datetime")]).count()
Out[203]:
                                                                        close  high  low  open  volume
symbol                                             datetime
British pound - United States dollar               2017-11-01 22:00:00      2     2    2     2       2
European Monetary Union Euro - United States do... 2017-11-01 22:00:00      2     2    2     2       2
United States dollar - Swiss franc                 2017-11-01 22:00:00      1     1    1     1       1

PS DocString for TimeGrouper could be bit more detailed:

In [204]: pd.TimeGrouper?
Init signature: pd.TimeGrouper(*args, **kwargs)
Docstring:
Custom groupby class for time-interval grouping

Parameters
----------
freq : pandas date offset or offset alias for identifying bin edges
closed : closed end of interval; left or right
label : interval boundary to use for labeling; left or right
nperiods : optional, integer
convention : {'start', 'end', 'e', 's'}
    If axis is PeriodIndex

It looks better for pd.Grouper :

In [205]: pd.Grouper?
Init signature: pd.Grouper(*args, **kwargs)
Docstring:
A Grouper allows the user to specify a groupby instruction for a target
object

This specification will select a column via the key parameter, or if the
level and/or axis parameters are given, a level of the index of the target
object.

These are local specifications and will override 'global' settings,
that is the parameters axis and level which are passed to the groupby
itself.

Parameters
----------
key : string, defaults to None
    groupby key, which selects the grouping column of the target
level : name/number, defaults to None
    the level for the target index
freq : string / frequency object, defaults to None
    This will groupby the specified frequency if the target selection
    (via key or level) is a datetime-like object. For full specification
    of available frequencies, please see `here
    <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`_.
axis : number/name of the axis, defaults to 0
sort : boolean, default to False
    whether to sort the resulting labels

additional kwargs to control time-like groupers (when freq is passed)

closed : closed end of interval; left or right
label : interval boundary to use for labeling; left or right
convention : {'start', 'end', 'e', 's'}
    If grouper is PeriodIndex

Returns
-------
A specification for a groupby instruction

Examples
--------

Syntactic sugar for ``df.groupby('A')``

>>> df.groupby(Grouper(key='A'))

Specify a resample operation on the column 'date'

>>> df.groupby(Grouper(key='date', freq='60s'))

Specify a resample operation on the level 'date' on the columns axis
with a frequency of 60s